Considerations for Embedding Inclusive Research Principles in the Design and Execution of Clinical Trials

Patient-centricity in digital measure development: co-evolution of best practice and regulatory guidance

Digital health technologies (DHTs) have the potential to modernize drug development and clinical trial operations by remotely, passively, and continuously collecting ecologically valid evidence that is meaningful to patients' lived experiences. Such evidence holds potential for all drug development stakeholders, including regulatory agencies, as it will help create a stronger evidentiary link between approval of new therapeutics and the ultimate aim of improving patient lives. However, only a very small number of novel digital measures have matured from exploratory usage into regulatory qualification or efficacy endpoints. This shows that despite the clear potential, actually gaining regulatory agreement that a new measure is both fit-for-purpose and delivers value remains a serious challenge. One of the key stumbling blocks for developers has been the requirement to demonstrate that a digital measure is meaningful to patients. This viewpoint aims to examine the co-evolution of regulatory guidance in the United States (U.S.) and best practice for integration of DHTs into the development of clinical outcome assessments. Contextualizing guidance on meaningfulness within the larger shift towards a patient-centric drug development approach, this paper reviews the U.S. Food and Drug Administration (FDA) guidance and existing literature surrounding the development of meaningful digital measures and patient engagement, including the recent examples of rejections by the FDA that further emphasize patient-centricity in digital measures. Finally, this paper highlights remaining hurdles and provides insights into the established frameworks for development and adoption of digital measures in clinical research.

Lifetime history of major depressive disorder is associated with decreased reward learning: Evidence from a novel online version of the probabilistic reward task

BACKGROUND

The Probabilistic Reward Task (PRT) is a signal detection task that assesses reward learning. In laboratory versions of the task, individuals with current or past major depressive disorder (MDD) were characterized by reduced response bias towards a more frequently rewarded stimuli, compared to controls. Our main goal was to develop and validate a novel online version of the PRT, and, in exploratory analyses, evaluate whether lifetime history of depression was associated with blunted reward learning.

METHODS

429 participants recruited via CloudResearch completed questionnaires assessing psychiatric history and an online PRT featuring visually appealing stimuli. 108 participants reported either current or past diagnosis of MDD (lifetime MDD group), and were compared to 321 without lifetime MDD.

RESULTS

Participants showed overall increase in response bias, validating the online PRT. Females with lifetime MDD (N = 43), compared to females without prior history of MDD (N = 173), exhibited blunted response bias towards the more frequently rewarded stimulus (i.e., reduced reward learning).

LIMITATIONS

Participants did not undergo a structured clinical interview, thus we cannot confirm whether they met full diagnostic criteria for depression.

CONCLUSIONS

The online PRT yielded similar psychometric properties as laboratory versions of the task. In exploratory analyses, females with lifetime MDD showed a lower propensity to modulate behavior as a function of rewards, which might contribute to heightened vulnerability for developing MDD in females. Future studies should consider social, cultural, and neurobiological factors contributing to sex differences in reward responsiveness and how factors may relate to disease prognosis and treatment outcomes.

Quantifying Clinical Trial Diversity in Pivotal Registration Trials of FDA Novel Drug Approvals

BACKGROUND

Health care inequity includes the lack of adequate representation of various populations in clinical trials. Government, academic and industry organizations have highlighted these issues and committed to actions to improve. In order to assess the current status and future success of these initiatives a quantitative objective measure to assess the state of clinical trial diversity is needed.

METHODS

FDA review documents for all novel drug approvals from January 2022 through March 2023 were assessed using a scorecard that considers diversity across different demographic subgroups including age (≥ 65 years old), sex (female), race (Black and Asian) and ethnicity (Hispanic/Latino). The scorecard assigns each drug a letter grade, between A and F, for each subgroup (and overall) based on (1) the percent of each sub-population included in the trials and grades relative to the percent of the US population per the 2020 Census, (2) the number of participants from each subpopulation that received the novel new drug in the trials, (3) the incidence or prevalence of the disease/condition in each of the sub-populations.

RESULTS

The FDA approved 49 novel new drugs for 50 indications (one drug was simultaneously approved for two indications). There was good representation of elderly and females with only two drugs receiving a D grade in either of these sub-populations. In contrast, Black (5 F grades) and Hispanic (4 F grades) representation was often inadequate. There were 10 drugs (20.0%) where there were no Black participants receiving the novel new drug and 16 (32.0%) approvals where there were 1-9 Black participants receiving the novel drug. In the Hispanic/Latino population there were 4 (8.0%) approvals with no Hispanic participants receiving the novel drug and 15 (30.0%) approvals where there were 1-9 Hispanic participants receiving the drug.

CONCLUSIONS

This scorecard provides an objective quantitative approach to assess the current state of diversity in clinical trials supporting new drug approvals. Substantial improvement in racial and ethnic representation is needed. Meaningful change will require actions and cooperation among all stakeholders to address this multifaceted issue and will take commitment, perseverance, and appropriate incentives.

Toward Inclusivity in Preclinical Drug Development: A Proposition to Start with Intestinal Organoids

Representation of humans from diverse backgrounds in the drug development process is key to advancing health equity, and while clinical trial design has recently made strides toward greater inclusivity, preclinical drug development has struggled to make those same gains. One barrier to inclusion is the current lack of robust and established in vitro model systems that simultaneously capture the complexity of human tissues while representing patient diversity. Here, the use of primary human intestinal organoids as a mechanism to advance inclusive preclinical research is proposed. This in vitro model system not only recapitulates tissue functions and disease states, but also retains the genetic identity and epigenetic signatures of the donors from which they are derived. Thus, intestinal organoids are an ideal in vitro prototype for capturing human diversity. In this perspective, the authors call for an industry-wide effort to leverage intestinal organoids as a starting point to actively and intentionally incorporate diversity into preclinical drug programs.

Precision Recruitment and Engagement of Individuals at Risk for Diabetes and Hypertension in Clinical Trials (PREDHICT): A Randomized Trial for an E-Persuasive Mobile Application to Inform Decision Making about Clinical Trials

The primary objective of this randomized trial was to test the effectiveness of the PREDHiCT digital application, which provides educational and supportive navigation to increase willingness to participate in a future clinical trial. The second objective was to test whether PREDHiCT increased clinical trial literacy or enhanced psychological facilitators of clinical trial participation, such as altruism. To test these two objectives, we conducted a 1-month remote decentralized trial with 100 participants who either have a personal or family history of cardiometabolic health conditions, such as hypertension, diabetes, and obesity. Results indicated significant changes in altruism (mean: -2.94 vs. 0.83; p-value = 0.011) and clinical trial literacy (mean: 0.55 vs. 2.59; p-value = 0.001) from baseline to 1-month follow-up between the control and intervention groups. Additionally, participants exposed to personalized clinical trial navigation had greater clinical trial literacy at the end of the study relative to the individuals in the control arm of the study. Our findings indicate that tailored education, navigation, and access to clinical trials-three unique features of our PREDHiCT app-increased altruism and clinical trial literacy but not willingness to participate in a trial.

Clinical Research Informatics: Contributions from 2022

OBJECTIVES

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.

METHOD

A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

RESULTS

Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).

CONCLUSIONS

The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.

Racial and ethnic disparities in clinical trials and peripheral artery disease

As peripheral artery disease (PAD) continues to affect many people in our population, the search continues for medical advances to improve the treatment algorithms for this debilitating disease. Prior research studies have more than adequately documented the disparity that exists in the outcomes of patients with PAD based on race and ethnicity. One of the paths to clinical improvement and removal of the disparate outcomes is through application of the studied variables in a well-constructed clinical trial. Unfortunately, the groups that are known for worse outcomes are also those with limited enrollment into clinical trials, which exacerbates the problem. This article evaluates the issue of underrepresentation of racial and ethnic minority populations in clinical trials, addresses factors contributing to the problem, and provides possible pathways to a viable solution.